In recently years, there has been some research [1]}, [2]} pointing out that the observed implicit feedback could be easily corrupted by different factors, such as popularity bias, conformity bias, exposure bias and position bias[3]}. Training recommenders with corrupted implicit data would lead to misunderstanding of the real user preference and sub-optimal recommendation performance [4]}, [5]}.
As a result, there have been some efforts aiming to address the noisy problem, which can be categorized into re-sampling methods [6]}, [7]}, [8]}, [9]}, [10]}, re-weighting methods [4]}, [12]}, [13]} and methods utilizing additional knowledge input [14]}, [5]}, [16]}.
Re-sampling methods aim to design more effective samplers for implicit feedback. For example, [7]} proposed to sample popular but not interacted items as negative examples while [9]} proposed that the viewed but not purchased items are highly likely to be real negative. [10]}, [20]} proposed to use reinforcement learning for negative sampling. The performance of re-sampling depends heavily on the sampling strategy [21]}, which is usually developed heuristically [6]}. Re-weighting methods
usually identify the noisy examples as samples with high loss values and then assign lower weights to them. But the denoising signal contained in loss values could encounter difficulties to distinguish noisy samples and hard clean samples [23]}. Besides, additional knowledge such as dwell time[14]}, gaze pattern[5]} and auxiliary item features[16]} can also be used to denoise implicit feedback, which, however, is usually expensive to collect.
